Here, a hybrid approach is proposed, integrating a hierarchical convolutional neural network with feature preserving and the contrast-based autotuned thresholding (CBAT) technique to identify surface cracks of roads, bridges or pavements via aerial photography, obtained by a formation of unmanned aerial vehicles (UAV) [1]}. The collected images are processed by the proposed neural network first for a probability map of a potential defect and then its features are further extracted by CBAT binarization for identification. Experiments on different datasets from [2]} and on images captured by our UAVs during the inspection of a monorail bridge indicate the advantage of the hierarchical convolutional neural network with feature preservation (HCNNFP) proposed in comparison with some crack detection approaches available in the existing literature. Various frameworks for crack detection and specific datasets are considered in a number of experiments for comprehensive assessment on the merits of our HCNNFP as well as its improvement over other post-processing methods.
